Biblio
The recent analysis indicates more than 250,000 people in the United States of America (USA) die every year because of medical errors. World Health Organisation (WHO) reports states that 2.6 million deaths occur due to medical and its prescription errors. Many of the errors related to the wrong drug/dosage administration by caregivers to patients due to indecipherable handwritings, drug interactions, confusing drug names, etc. The espousal of Mobile-based speech recognition applications will eliminate the errors. This allows physicians to narrate the prescription instead of writing. The application can be accessed through smartphones and can be used easily by everyone. An application program interface has been created for handling requests. Natural language processing is used to read text, interpret and determine the important words for generating prescriptions. The patient data is stored and used according to the Health Insurance Portability and Accountability Act of 1996 (HIPAA) guidelines. The SMS4-BSK encryption scheme is used to provide the data transmission securely over Wireless LAN.
This article describes the development of two mobile applications for learning Digital Electronics. The first application is an interactive app for iOS where you can study the different digital circuits, and which will serve as the basis for the second: a game of questions in augmented reality.
The development in the web technologies given growth to the new application that will make the voting process very easy and proficient. The E-voting helps in providing convenient, capture and count the votes in an election. This project provides the description about e-voting using an Android platform. The proposed e-voting system helps the user to cast the vote without visiting the polling booth. The application provides authentication measures in order to avoid fraud voters using the OTP. Once the voting process is finished the results will be available within a fraction of seconds. All the casted vote count is encrypted using AES256 algorithm and stored in the database in order to avoid any outbreaks and revelation of results by third person other than the administrator.
Video surveillance plays an important role in our times. It is a great help in reducing the crime rate, and it can also help to monitor the status of facilities. The performance of the video surveillance system is limited by human factors such as fatigue, time efficiency, and human resources. It would be beneficial for all if fully automatic video surveillance systems are employed to do the job. The automation of the video surveillance system is still not satisfying regarding many problems such as the accuracy of the detector, bandwidth consumption, storage usage, etc. This scientific paper mainly focuses on a video surveillance system using Convolutional Neural Networks (CNN), IoT and cloud. The system contains multi nods, each node consists of a microprocessor(Raspberry Pi) and a camera, the nodes communicate with each other using client and server architecture. The nodes can detect humans using a pretraining MobileNetv2-SSDLite model and Common Objects in Context(COCO) dataset, the captured video will stream to the main node(only one node will communicate with cloud) in order to stream the video to the cloud. Also, the main node will send an SMS notification to the security team to inform the detection of humans. The security team can check the videos captured using a mobile application or web application. Operating the Object detection model of Deep learning will be required a large amount of the computational power, for instance, the Raspberry Pi with a limited in performance for that reason we used the MobileNetv2-SSDLite model.
Mobile crowd sensing (MCS) is a rapidly developing technique for information collection from the users of mobile devices. This technique deals with participants' personal information such as their identities and locations, thus raising significant security and privacy concerns. Accordingly, anonymous authentication schemes have been widely considered for preserving participants' privacy in MCS. However, mobile devices are easy to lose and vulnerable to device capture attacks, which enables an attacker to extract the private authentication key of a mobile application and to further invade the user's privacy by linking sensed data with the user's identity. To address this issue, we have devised a special anonymous authentication scheme where the authentication request algorithm can be obfuscated into an unintelligible form and thus the authentication key is not explicitly used. This scheme not only achieves authenticity and unlinkability for participants, but also resists impersonation, replay, denial-of-service, man-in-the-middle, collusion, and insider attacks. The scheme's obfuscation algorithm is the first obfuscator for anonymous authentication, and it satisfies the average-case secure virtual black-box property. The scheme also supports batch verification of authentication requests for improving efficiency. Performance evaluations on a workstation and smart phones have indicated that our scheme works efficiently on various devices.
Although the importance of mobile applications grows every day, recent vulnerability reports argue the application's deficiency to meet modern security standards. Testing strategies alleviate the problem by identifying security violations in software implementations. This paper proposes a novel testing methodology that applies state machine learning of mobile Android applications in combination with algorithms that discover attack paths in the learned state machine. The presence of an attack path evidences the existence of a vulnerability in the mobile application. We apply our methods to real-life apps and show that the novel methodology is capable of identifying vulnerabilities.
The paper introduces a smart system developed with sensors that is useful for internal and external security. The system is useful for people living in houses, apartments, high officials, bank, and offices. The system is developed in two phases one for internal security like home another is external security like open areas, streets. The system is consist of a mobile application, capacitive sensing, smart routing these valuable features to ensure safety of life and wealth. This security system is wireless sensor based which is an effective alternative of cctv cameras and other available security systems. Efficiency of this system is developed after going through practical studies and prototyping. The end result explains the feasibility rate, positive impact factor, reliability of the system. More research is possible in future based on this system this research explains that.
Named Data Networking (NDN) is a content-oriented future Internet architecture, which well suits the increasingly mobile and information-intensive applications that dominate today's Internet. NDN relies on in-network caching to facilitate content delivery. This makes it challenging to enforce access control since the content has been cached in the routers and the content producer has lost the control over it. Due to its salient advantages in content delivery, network coding has been introduced into NDN to improve content delivery effectiveness. In this paper, we design ACNC, the first Access Control solution specifically for Network Coding-based NDN. By combining a novel linear AONT (All Or Nothing Transform) and encryption, we can ensure that only the legitimate user who possesses the authorization key can successfully recover the encoding matrix for network coding, and hence can recover the content being transmitted. In addition, our design has two salient merits: 1) the linear AONT well suits the linear nature of network coding; 2) only one vector of the encoding matrix needs to be encrypted/decrypted, which only incurs small computational overhead. Security analysis and experimental evaluation in ndnSIM show that our design can successfully enforce access control on network coding-based NDN with an acceptable overhead.
Route planner systems support commuters and city visitors in finding the best route between two arbitrary points. More advanced route planners integrate different transportation modes such as private transport, public transport, car- and bicycle sharing or walking and are able combine these to multi-modal routes. Nevertheless, state-of-the-art planner systems usually do not consider the users' personal preferences or the wisdom of the crowd when suggesting multi-modal routes. Including the knowledge and experience of locals who are familiar with local transport allows identification of alternative routes which are, for example, less crowded during peak hours. Collaborative filtering (CF) is a technique that allows recommending items such as multi-modal routes based on the ratings of users with similar preferences. In this paper, we introduce RouteMe, a mobile recommender system for personalized, multi-modal routes which combines CF with knowledge-based recommendations to increase the quality of route recommendations. We present our hybrid algorithm in detail and show how we integrate it in a working prototype. The results of a user study show that our prototype combining CF, knowledge-based and popular route recommendations outperforms state-of-the-art route planners.
Cloud computing is significantly reshaping the computing industry built around core concepts such as virtualization, processing power, connectivity and elasticity to store and share IT resources via a broad network. It has emerged as the key technology that unleashes the potency of Big Data, Internet of Things, Mobile and Web Applications, and other related technologies; but it also comes with its challenges - such as governance, security, and privacy. This paper is focused on the security and privacy challenges of cloud computing with specific reference to user authentication and access management for cloud SaaS applications. The suggested model uses a framework that harnesses the stateless and secure nature of JWT for client authentication and session management. Furthermore, authorized access to protected cloud SaaS resources have been efficiently managed. Accordingly, a Policy Match Gate (PMG) component and a Policy Activity Monitor (PAM) component have been introduced. In addition, other subcomponents such as a Policy Validation Unit (PVU) and a Policy Proxy DB (PPDB) have also been established for optimized service delivery. A theoretical analysis of the proposed model portrays a system that is secure, lightweight and highly scalable for improved cloud resource security and management.
Smart Card has complications with validation and transmission process. Therefore, by using peeping attack, the secret code was stolen and secret filming while entering Personal Identification Number at the ATM machine. We intend to develop an authentication system to banks that protects the asset of user's. The data of a user is to be ensured that secure and isolated from the data leakage and other attacks Therefore, we propose a system, where ATM machine will have a QR code in which the information's are encrypted corresponding to the ATM machine and a mobile application in the customer's mobile which will decrypt the encoded QR information and sends the information to the server and user's details are displayed in the ATM machine and transaction can be done. Now, the user securely enters information to transfer money without risk of peeping attack in Automated Teller Machine by just scanning the QR code at the ATM by mobile application. Here, both the encryption and decryption technique are carried out by using Triple DES Algorithm (Data Encryption Standard).
Ubiquitous WiFi infrastructure and smart phones offer a great opportunity to study physical activities. In this paper, we present MobiCamp, a large-scale testbed for studying mobility-related activities of residents on a campus. MobiCamp consists of \textasciitilde2,700 APs, \textasciitilde95,000 smart phones, and an App with \textasciitilde2,300 opt-in volunteer users. More specifically, we capture how mobile users interact with different types of buildings, with other users, and with classroom courses, etc. To achieve this goal, we first obtain a relatively complete coverage of the users' mobility traces by utilizing four types of information from SNMP and by relaxing the location granularity to roughly at the room level. Then the popular App provides user attributes (grade, gender, etc.) and fine-grained behavior information (phone usages, course timetables, etc.) of the sampled population. These detailed mobile data is then correlated with the mobility traces from the SNMP to estimate the entire campus population's physical activities. We use two applications to show the power of MobiCamp.
The record-and-replay approach for software testing is important and valuable for developers in designing mobile applications. However, the existing solutions for recording and replaying Android applications are far from perfect. When considering the richness of mobile phones' input capabilities including touch screen, sensors, GPS, etc., existing approaches either fall short of covering all these different input types, or require elevated privileges that are not easily attained and can be dangerous. In this paper, we present a novel system, called MobiPlay, which aims to improve record-and-replay testing. By collaborating between a mobile phone and a server, we are the first to capture all possible inputs by doing so at the application layer, instead of at the Android framework layer or the Linux kernel layer, which would be infeasible without a server. MobiPlay runs the to-be-tested application on the server under exactly the same environment as the mobile phone, and displays the GUI of the application in real time on a thin client application installed on the mobile phone. From the perspective of the mobile phone user, the application appears to be local. We have implemented our system and evaluated it with tens of popular mobile applications showing that MobiPlay is efficient, flexible, and comprehensive. It can record all input data, including all sensor data, all touchscreen gestures, and GPS. It is able to record and replay on both the mobile phone and the server. Furthermore, it is suitable for both white-box and black-box testing.
Applications in mobile marketplaces may leak private user information without notification. Existing mobile platforms provide little information on how applications use private user data, making it difficult for experts to validate appli- cations and for users to grant applications access to their private data. We propose a user-aware-privacy-control approach, which reveals how private information is used inside applications. We compute static information flows and classify them as safe/un- safe based on a tamper analysis that tracks whether private data is obscured before escaping through output channels. This flow information enables platforms to provide default settings that expose private data for only safe flows, thereby preserving privacy and minimizing decisions required from users. We build our approach into TouchDe- velop, an application-creation environment that allows users to write scripts on mobile devices and install scripts published by other users. We evaluate our approach by studying 546 scripts published by 194 users, and the results show that our approach effectively reduces the need to make access-granting choices to only 10.1 % (54) of all scripts. We also conduct a user survey that involves 50 TouchDevelop users to assess the effectiveness and usability of our approach. The results show that 90 % of the users consider our approach useful in protecting their privacy, and 54 % prefer our approach over other privacy-control approaches.
Mobile Apps running on smartphones and tablet pes offer a new possibility to enhance the work of engineers because they provide an easy-to-use, touchscreen-based handling and can be used anytime and anywhere. Introducing mobile apps in the engineering domain is difficult because the IT environment is heterogeneous and engineering-specific challenges in the app development arise e. g., large amount of data and high security requirements. There is a need for an engineering-specific middleware to facilitate and standardize the app development. However, such a middleware does not yet exist as well as a holistic set of requirements for the development. Therefore, we propose a design method which offers a systematic procedure to develop Mobile Engineering-Application Middleware.